The present invention relates generally to automatic machine-implemented fingerprint recognition systems, and more specifically to a system, method and computer program product for verifying a fingerprint-under-test against a set of reference fingerprints.
The use of biometrics as an aid to confirming authorization for access to various types of resources or locations has been increasing. Biometric systems measure various unique or nearly unique characteristics of a person's body to assist in confirming identity, and consequently in authorizing an access requested by the person. Common body characteristics used in these systems include fingerprints and eye retinal patterns.
Fingerprints are believed by many to be unique or nearly unique across the population base. Fingerprints include ridges/furrows that define a complex pattern. Each fingerprint typically includes many pattern features (including features referred to as minutia) that are cognizable by detection systems. These minutia serve as the basis by which many fingerprint biometric systems judge a match between a fingerprint-under-test and a reference fingerprint. That is, when the system determines that there is a sufficient match between the fingerprint-under-test and the reference, the system has determined that there are enough matching minutia between the two.
There are two common metrics used to measure how accurate these systems are when comparing the fingerprint-under-test with one or more reference fingerprints. These metrics are a false acceptance ratio (FAR) and a false rejection ratio (FRR). These metrics are dependent upon many factors including the implementation of the sensing system and the type of fingerprint features measured. In general, the FAR and the FRR of conventional systems are inversely related resulting in balancing the performance of these systems, and limiting the practicably achievable accuracy.
Further, processing multitudes of minutia information for multiple fingerprints using conventional systems requires significant processing resources and are often still viewed as taking excessive amounts of time. The significant processing resources needed relegate many solutions to systems that are tethered to desktop computer systems or other substantial processing system. However, there exist many uses for these systems that cannot be used in conjunction with desktop computer systems.
Various systems exist for compression of data to enable the data to be stored using fewer resources (e.g., memory or disk storage) or to enable the data to be transmitted quicker. There are both lossless and lossy systems that may be used based upon characteristics of the implementation. Lossless systems include variable-length encoding systems that, in general application, have a theoretical limit to the degree of compression.
What is needed is a system offering improved image compression and processing efficiency due to reduced size requirements using the often more limited processing resources of a stand-alone or embedded computing platform while desirably being cost-effective.